Analisis Sentimen Twitter Terhadap Isu Royalti Lagu di Industri Musik Indonesia Menggunakan Naive Bayes dan Support Vector Machine Berbasis TF-IDF
- Alif Fadhil Wibowo Universitas Dian Nuswantoro, Semarang, Indonesia
-
Ajib Susanto
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
(*) Corresponding Author
Abstract
The development of digital platforms in Indonesia’s music industry has triggered various debates regarding the song royalty system, particularly those related to copyright and income distribution for songwriters. Public opinions on these issues are widely expressed through Twitter, making it a valuable data source for sentiment analysis. This study aims to analyze public sentiment toward song royalty issues in the Indonesian music industry and compare the performance of Multinomial Naive Bayes and Support Vector Machine (SVM) algorithms using TF-IDF weighting. This study contributes through the implementation of semi-manual labeling, the use of a stratified 5-fold cross-validation approach, and multi-metric evaluation to obtain more representative sentiment classification results on song royalty issues in Indonesian social media. The initial dataset was collected through Twitter scraping using keywords related to song royalties and music copyright. The data were then processed through preprocessing stages, including case folding, cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using a semi-manual approach, involving lexicon-based pre-labeling followed by manual verification into three sentiment categories: positive, negative, and neutral. Model evaluation was performed using stratified 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm outperformed Multinomial Naive Bayes, achieving an accuracy of 93.21%, while Multinomial Naive Bayes obtained an accuracy of 82.53%. These findings demonstrate that SVM is more effective in handling high-dimensional textual data represented using TF-IDF for Indonesian sentiment analysis. This study is expected to provide insights into public perceptions regarding song royalty issues and serve as a reference for sentiment analysis applications on Indonesian social media data.
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References
F. Koto and J. H. Lau, “IndoBERT: A Pretrained Language Model for Indonesian NLP Tasks,” in Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 757–770. doi: 10.18653/v1/2020.coling-main.66.
R. Zhang and T. Lee, “Support Vector Machine for High-Dimensional Text Classification,” Pattern Recognit. Lett., vol. 145, pp. 123–130, 2021, doi: 10.1016/j.patrec.2021.02.018.
D. Wijaya and R. Prasetyo, “Natural Language Processing for Bahasa Indonesia Sentiment Analysis,” Procedia Comput. Sci., vol. 179, pp. 768–777, 2021, doi: 10.1016/j.procs.2021.01.065.
Ma’rufudin and A. Yudhistira, “Analisis Sentimen Petani Milenial Pada Media Sosial X Menggunakan Algoritma Support Vector Machine (SVM),” Jurnal Pendidikan dan Teknologi Indonesia, vol. 5, no. 3, pp. 845–857, 2025, doi: 10.52436/1.jpti.717.
L. Wang and M. Garcia, “Comparison of Machine Learning Algorithms for Text Classification,” IEEE Access, vol. 11, pp. 33120–33135, 2023, doi: 10.1109/ACCESS.2023.3267781.
H. Chen and A. Kumar, “Sentiment Analysis Using Machine Learning Approaches,” Expert Syst. Appl., vol. 218, p. 119553, 2023, doi: 10.1016/j.eswa.2023.119553.
R. Ramlan, N. Satyahadewi, and W. Andani, “Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM,” Jambura Journal of Mathematics, vol. 5, no. 2, pp. 431–445, 2023, doi: 10.34312/jjom.v5i2.20860.
W. A. Prabowo and F. Azizah, “Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 6, pp. 1142–1148, 2020, doi: 10.29207/resti.v4i6.2753.
M. Rodríguez-Ibáñez and others, “A review on sentiment analysis from social media platforms,” Expert Syst. Appl., vol. 223, p. 119878, 2023, doi: 10.1016/j.eswa.2023.119878.
M. Rahman and S. Karim, “Comparison of Machine Learning Algorithms for Sentiment Analysis on Twitter,” IEEE Access, vol. 10, pp. 55120–55134, 2022, doi: 10.1109/ACCESS.2022.3174412.
R. H. Muhammadi, T. G. Laksana, and A. B. Arifa, “Combination of Support Vector Machine and Lexicon-Based Algorithm in Twitter Sentiment Analysis,” Khazanah Informatika, vol. 8, no. 1, pp. 59–71, 2022, doi: 10.23917/khif.v8i1.15213.
G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf. Process. Manag., vol. 24, no. 5, pp. 513–523, 1988, doi: 10.1016/0306-4573(88)90021-0.
L. Agusta and A. Nugroho, “Implementation of TF-IDF and SVM for Indonesian Text Classification,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 4, pp. 221–228, 2021, doi: 10.14569/IJACSA.2021.0120428.
P. Singh and A. Kumar, “Performance Evaluation of Naive Bayes and SVM in Text Mining,” Journal of Information and Optimization Sciences, vol. 42, no. 5, pp. 1117–1128, 2021, doi: 10.1080/02522667.2021.1908902.
F. Sebastiani, “Machine Learning in Automated Text Categorization,” ACM Comput. Surv., vol. 34, no. 1, pp. 1–47, 2002, doi: 10.1145/505282.505283.
R. Zhang and T. Lee, “Support Vector Machine for High-Dimensional Text Classification,” Pattern Recognit. Lett., vol. 145, pp. 123–130, 2021, doi: 10.1016/j.patrec.2021.02.018.
F. Ahmed and J. Brown, “Support Vector Machine in High-Dimensional Sparse Data,” Machine Learning with Applications, vol. 8, p. 100303, 2022, doi: 10.1016/j.mlwa.2022.100303.
R. Hilma, M. Ula, and S. Fachrurrazi, “Analisis Sentimen Cyberbullying pada Media Sosial Twitter Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier,” e-jurnal TECHSI, vol. 14, no. 2, pp. 107–123, 2023, doi: 10.29103/techsi.v14i2.12103.
Y. Li and R. Zhang, “Recent Advances in Sentiment Analysis Using Machine Learning,” IEEE Access, vol. 10, pp. 99812–99830, 2022, doi: 10.1109/ACCESS.2022.3201445.
K. Kowsari and others, “Text Classification Algorithms: A Survey,” Information, vol. 10, no. 4, p. 150, 2019, doi: 10.3390/info10040150.
T. Safitri, Y. Umaidah, and I. Maulana, “Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine,” Journal of Applied Informatics and Computing, vol. 7, no. 1, pp. 28–35, 2023, doi: 10.30871/jaic.v7i1.5039.
N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM),” Jurnal Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.
H. Atsqalani, N. Hayatin, and C. S. K. Aditya, “Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine and Query Expansion Ranking,” JOIN (Jurnal Online Informatika), vol. 7, no. 1, pp. 116–122, 2022, doi: 10.15575/join.v7i1.669.
T. A. Rana and Y. N. Cheah, “A Systematic Literature Review on Text Preprocessing Techniques for Sentiment Analysis,” Artif. Intell. Rev., vol. 55, no. 3, pp. 1–39, 2022, doi: 10.1007/s10462-021-10086-y.
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